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We propose two efficient algorithms for exploring topic diversity in large document corpora such as user generated content on the social web, bibliographic data, or other web repositories. Analyzing diversity is useful for obtaining insights into knowledge evolution, trends, periodicities, and topic heterogeneity of such collections. Calculating diversity statistics requires averaging over the similarity of all object pairs, which, for large corpora, is prohibitive from a computational point of view. Our proposed algorithms overcome the quadratic complexity of the average pair-wise similarity computation, and allow for constant time (depending on dataset properties) or linear time approximation with probabilistic guarantees.

This powerful reporting library aims to simplify producing reports using iText. The most demanding and coding intensive tasks when producing reports with iText are how to get from data to report elements, how to style those blocks and how to evaluate (debug) the resulting report layout. Exactly those three tasks are simplified by this library: xml configuration and/or annotations to transform existing Java data objects into report block, provide styling information (css like, syntax independent) seperate from code, a firebug like debug mode to help evaluate your report.